Understanding and estimating the energy consumed by machining are essential tasks as the energy consumption during machining is responsible for a substantial part of the environmental burden in manufacturing industry. Facing the problem, the present paper aims to analyse the correlation between numerical control (NC) codes and energy-consuming components of machine tools, and to propose a practical method for estimating the energy consumption of NC machining. Each energy-consuming component is respectively estimated by considering its power characteristics and the parameters extracted from the NC codes, and then the procedure estimating energy consumption is developed by accounting for the total energy consumption of the components via the NC program. The developed method is verified by comparing the estimated energy consumption with the actual measurement results of machining two test workpieces on two different machine tools, an NC milling machine and an NC lathe, and is also applied to evaluate the energy consumption of two different NC programs on the NC milling machine. The results obtained show that the method is efficient and practical, and can help process planning designers make robust decisions in choosing an effective energy-efficient NC program.

Cerebral hemorrhage, a difficult issue in clinical practice, is often detected and studied with computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, these expensive devices are not readily available in economically underdeveloped regions, and hence are unable to provide bedside and emergency on-site monitoring. The magnetic inductive phase shift (MIPS) is an emerging technology that may become a new tool to detect cerebral hemorrhage and to serve as an inexpensive partial substitute to medical imaging. In order to study a wider band of cerebral hemorrhage MIPS and to provide more useful information for measuring cerebral hemorrhage, we established a cerebral hemorrhage magnetic induction phase shift spectroscopy (MIPSS) detection system. Thirteen rabbits with five cerebral hemorrhage states were studied using a single coil-coil within a 1 MHz-200 MHz frequency range in linear sweep. A feature band (FB) with the highest detection sensitivity and the greatest stability was selected for further analysis and processing. In addition, a maximum conductivity cerebrospinal fluid (CSF) MRI was performed to verify and interpret the MIPSS result. The average phase shift change induced by a 3 ml injection of autologous blood under FB was -7.7503° ± 1.4204°, which was considerably larger than our previous work. Data analysis with a non-parametric statistical Friedman M test showed that in the FB, MIPSS could distinguish the five states of cerebral hemorrhage in rabbits, with a statistical significance of p<0.05. A B-F distribution profile was designed according to the MIPSS under FB that can provide instantaneous diagnostic information about the cerebral hemorrhage severity from a single set of measurements. The results illustrate that the MIPSS detection method is able to provide a new possibility for real-time monitoring and diagnosis of the severity of cerebral hemorrhage.

Cerebral hemorrhage is an important clinical problem that is often monitored and studied with expensive techniques, such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). These devices are not readily available in economically underdeveloped regions of the world and in emergency departments and emergency zones. The magnetic inductive method is an emerging technology that may become a new tool to detect cerebral hemorrhage. In this study, a special phase detector (PD) was developed and used for cerebral hemorrhage detection with the magnetic inductive method. The performance indicated that the PD can achieve phase noise as low as 6 m° and a 4-hour phase drift as low as 30 m° at 21.4 MHz. The noise and drift decreased as the frequency decreased. The performance at 10.7 MHz was slightly better than that of other recently developed phase detection systems. To test the practicality of the system, the PD was used to detect the volume change in a self-made physical model of the brain. The measured phase shift was approximately proportional to the volume change of physiological saline inside the model. The change of the phase shift increased as the volume change and frequency increased. The results are in agreement with those from previous reports. To verify the feasibility of in vivo detection, an autologous blood injection model was established in rabbit brain. The results from the injection group showed a similar trend of increasing phase shift change with increasing injection volume. The average phase shift change induced by a 3-ml injection of blood was 0.502°±0.119°, which was much larger than that of the control group. The measurement system can distinguish a minimal cerebral hemorrhage volume of approximately 0.5 ml. All of the results demonstrated that the PD used with this method can detect cerebral hemorrhage.

Energy consumption of machine tool has drawn wide attention in recent years. The additional load losses of machine tools are of great importance for investigating the energy consumption of machine tools because those account for 15-20% of the cutting power and may even be up to nearly 30% of the cutting power in our researches. For lack of adequate understanding of the characteristics of additional load losses in the past, the additional load losses coefficient, defined as the ratio of additional load losses to cutting power, was regarded as a constant while the spindle speed was unchanged. However, it is discovered in our practical measurements that it is not so. In this paper, it proposes an additional load losses model based on power flow model, under the condition of the slip of spindle motor being small, in order to fully understand the characteristics of additional load losses. The characteristics of additional load losses include the relationship between additional load losses and cutting power, the relationship between additional load losses and spindle speed, and the relationship between additional load losses and cutting torque. Further more, an experimental system is developed to acquire the additional load losses through measuring cutting torque, spindle speed and input power of machine tool. As an example, several experiments are carried out on the CNC lathe by adjusting cutting parameters including spindle speed, feed rate and cutting depth. The experimental results show that the additional load losses coefficient varies with spindle speed and cutting torque, which can be fitted by a 1st order polynomial.

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